The present invention relates generally to pattern recognition systems, and more particularly to, a system and method for improving sorting performance of various parts using cluster analysis.
Various parts are utilized by customers to fabricate more complex assemblies. Customers utilize various testing methods to identify both “good” and “bad” parts prior to the parts being installed into the more complex assembly. The good parts conform to the manufacturer's specifications. Whereas, the bad parts do not conform to the customers specifications.
To identify both the good parts and the bad parts, the customer typically provides an outside testing facility, for example, with a set of known good and bad parts. The set of known good and bad parts is referred to herein as a “training set”. The testing facility utilizes a conventional system to collect various data on the training set. The data is then stored in a database in the conventional system. The data collected for each part includes the frequency of a resonance or peak, and the values for that peak, such as amplitude, zero-crossing width, etc. The resultant frequency values for all of the selected peaks are then transmitted to a conventional statistical analysis/pattern recognition (VIPR) tool.
The VIPR tool utilizes the resultant frequencies to identify a Mahalanobis Taguchi System/Mahalanobis Distance (MTS). The MTS Distance is used to identify a subset of frequencies that can be used to find a difference between the provided good and bad parts and optimize a threshold distance to accept as many good parts as possible and reject as many bad parts as possible. This initial MTS test uses only the good parts to create its test and then assigns an “MTS distance” for every part (including the bad parts), which is the distance the part is from the “center” of the good parts.
The MTS Distance is a multi-dimensional value that may be represented as an ellipse in a two-dimensional (2D) figure and as an “egg-shape” in a three-dimensional (3D) figure, with all parts inside the “egg” passing the MTS test (the good parts) and all of the parts outside the “egg” failing the MTS test (the bad parts). Typically 3-7 frequencies (dimensions) are used by the MTS test to identify the good and bad parts. In the case that some bad parts are accepted, an additional “bias test” may be used to reject these additional bad parts. The bias test is performed by calculating an additional MTS distance, using the frequencies for only the bad parts. As a result, two MTS distances are calculated for each part, the distance the part is from the “goods center ellipsoid” and the distance the part is from the “bads center ellipsoid.” A ratio of the two MTS distances is used to reject additional parts. More generally, if conventional system determines that the part is closer to the center of the bads center ellipsoid, the part is more likely to be a bad or non-conforming part, and is rejected. The VIPR then returns a list of possible solutions that may include the 3-7 frequencies used to distinguish the good parts from the bad parts. The possible set of solutions is referred to as a “VIPR Score.” The VIPR score is a sum of the probabilities of good parts passing and bad parts failing the MTS and Bias tests.
A conventional validation tool utilizes the VIPR score to ensure that all of the above described peaks can be selected properly and also ensure that there are no conflicts or problems with the VIPR solution. The validation tool determines which peaks may be used to predict a window to look for the next peak and organizes the order that the peaks are swept in an optimal way. Each potential sort is given a Validation score that is the sum of the probabilities that the good parts will pass the tests and the bad parts will fail the tests with real data, not just frequencies. A user inputs the solution “into production”, meaning that the solution is used on unknown parts received from new production. When the customer's process changes or the results are no longer acceptable data is taken on new, classified parts, and the whole process is repeated.
However, the conventional VIPR system is generally less effective when processing parts having a large process variation. More specifically, the variation in the good parts becomes so large that the variation obscures the differences between the good and bad parts. Therefore, VIPR is forced to group all of these good parts, which may not be that similar, into one large group. Because VIPR utilizes a single large group to identify the good parts, the boundary for the large group may become so large that bad parts may be inadvertently included in the grouping of good parts.